import numpy as np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.optimizers import RMSprop

(X_train, y_train), (X_test, y_test) = mnist.load_data()

X_train = X_train.reshape(X_train.shape[0], -1) / 255.
X_test = X_test.reshape(X_test.shape[0], -1) / 255.
y_train = np_utils.to_categorical(y_train, num_classes=10)
y_test = np_utils.to_categorical(y_test, num_classes=10)

model = Sequential([
    Dense(32, input_dim=784),
    Activation('relu'),
    Dense(10),
    Activation('softmax')
])

rmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)

model.compile(optimizer=rmsprop, loss='categorical_crossentropy', metrics=['accuracy'])

print('Training ------------')

model.fit(X_train, y_train, epochs=2, batch_size=32)

print('\nTesting ------------')

loss, accuracy = model.evaluate(X_test, y_test)
print('test loss: ', loss)
print('test accuracy: ', accuracy)
